ArticleRSC advances2019
Chemical space exploration guided by deep neural networks.
Article in RSC advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The trial behind it
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Who cites it
27 citing papers in PubMed.
- Identification of new ER-targeted anticancer hits active against MCF-7 breast cancer cells in the marine fungal secondary metabolite library using computative and experimental approaches.Journal of computer-aided molecular design · 2026Article
- Efficient visualization of chemical space.Chemical science · 2026Article
- A Quantitative Electrostatic Potential Descriptor Enables Deep Learning-Accelerated Discovery of High-Performance Lithium-Ion Battery Electrolytes.Angewandte Chemie (International ed. in English) · 2026Article
- From Chemical Scaffold to Predicted Toxicity: A Scaffold-Aware Pipeline for Reliable In Silico Prioritization ofInternational journal of molecular sciences · 2026Article
- Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes.Journal of cheminformatics · 2026Review
- Isolation, Bromination, and Antimicrobial Activity of 3-Ethyl-4-hydroxy-6-methyl-Antibiotics (Basel, Switzerland) · 2026Article
- Analysis of the Toxicological Profile ofPlants (Basel, Switzerland) · 2025Article
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.Molecular informatics · 2025Article
- MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models.Journal of cheminformatics · 2024Article
- The macrocycle inhibitor landscape of SLC-transporter.Molecular informatics · 2024Article
- Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows.ACS polymers Au · 2023Review
- Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets.Chemical research in toxicology · 2023Article
- Improvement of multi-task learning by data enrichment: application for drug discovery.Journal of computer-aided molecular design · 2023Article
- Artificial intelligence for drug discovery: Resources, methods, and applications.Molecular therapy. Nucleic acids · 2023Review
- Scaffold and Structural Diversity of the Secondary Metabolite Space of Medicinal Fungi.ACS omega · 2023Article
- An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling.Computational and structural biotechnology journal · 2023Review
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Article
- Exploring Chemical Reaction Space with Reaction Difference Fingerprints and Parametric t-SNE.ACS omega · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A parametric t-SNE approach based on deep feed-forward neural networks was applied to the chemical space visualization problem. It is able to retain more information than certain dimensionality reduction techniques used for this purpose (principal component analysis (PCA), multidimensional scaling (MDS)). The applicability of this method to some chemical space navigation tasks (activity cliffs and activity landscapes identification) is discussed. We created a simple web tool to illustrate our work (http://space.syntelly.com).
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.